Multi-document summarization

About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.

Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning.

Abstract: While neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (MDS). We observe two major challenges when adapting SDS advances to MDS: (1) MDS involves larger search space and yet more limited training data, setting obstacles for neural methods to learn adequate representations; (2) MDS needs to resolve higher information redundancy among the source documents, which SDS methods are less effective to handle. To close the gap, we present RL-MMR, Maximal Margin Relevance-guided Reinforcement Learning for MDS, which unifies advanced neural SDS methods and statistical measures used in classical MDS. RL-MMR casts MMR guidance on fewer promising candidates, which restrains the search space and thus leads to better representation learning. Additionally, the explicit redundancy measure in MMR helps the neural representation of the summary to better capture redundancy. Extensive experiments demonstrate that RL-MMR achieves state-of-the-art performance on benchmark MDS datasets. In particular, we show the benefits of incorporating MMR into end-to-end learning when adapting SDS to MDS in terms of both learning effectiveness and efficiency.

Trends in Extractive and Abstractive Techniques in Text Summarization

Abstract: Text Summarization was proved to be an advantage over manually summarizing the large data. It condenses the salient features from the text by preserving the content and serves the meaningful summary. Classification can be done in two ways - extractive and abstractive summarization. Extractive summarization uses statistical and linguistic features to determine the important features and fuse them into a shorter version. Whereas abstractive summarization understands the whole document and then generates the summary. In this paper extractive and abstractive methods are framed. Keywordssummarization, Abstractive summarization

Automatic Summarization for Financial News Delivery on Mobile Devices.

Abstract: Christopher C C Yang and Fu Lee Wang Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China {yang, flwang}@secuhkeduhk ABSTRACT Wireless access with mobile devices is a promising addition to the WWW and traditional electronic business Mobile devices provide convenience and portable access to the huge information space on the Internet It is desire to access the most updated financial information through mobile devices in order to make critical and urgent decision for most of the investors In this paper, we present a financial news delivery system on mobile devices based on the fractal summarization model Fractal summarization is developed based on the fractal theory It generates a brief skeleton of summary at the first stage, and the details of the summary on different levels of the document are generated on demands of users Such interactive summarization reduces the computation load in comparing with the generation of the entire summary in one batch by the traditional summarization, which is ideal for wireless access

Application of Text Summarization techniques to the Geographical Information Retrieval task

Abstract: Automatic Text Summarization has been shown to be useful for Natural Language Processing tasks such as Question Answering or Text Classification and other related fields of computer science such as Information Retrieval. Since Geographical Information Retrieval can be considered as an extension of the Information Retrieval field, the generation of summaries could be integrated into these systems by acting as an intermediate stage, with the purpose of reducing the document length. In this manner, the access time for information searching will be improved, while at the same time relevant documents will be also retrieved. Therefore, in this paper we propose the generation of two types of summaries (generic and geographical) applying several compression rates in order to evaluate their effectiveness in the Geographical Information Retrieval task. The evaluation has been carried out using GeoCLEF as evaluation framework and following an Information Retrieval perspective without considering the geo-reranking phase commonly used in these systems. Although single-document summarization has not performed well in general, the slight improvements obtained for some types of the proposed summaries, particularly for those based on geographical information, made us believe that the integration of Text Summarization with Geographical Information Retrieval may be beneficial, and consequently, the experimental set-up developed in this research work serves as a basis for further investigations in this field.

NewsGist: a multilingual statistical news summarizer

Abstract: In this paper we present NewsGist, a multilingual, multidocument news summarization system underpinned by the Singular Value Decomposition (SVD) paradigm for document summarization and purpose-built for the Europe Media Monitor (EMM). The summarization method employed yielded state-ofthe-art performance for English at the Update Summarization task of the last Text Analysis Conference (TAC) 2009 and integrated with EMM represents the first online summarization system able to produce summaries for so many languages. We discuss the context and motivation for developing the system and provide an overview of its architecture. The paper is intended to serve as accompaniment of a live demo of the system, which can be of interest to researchers and engineers working on multilingual open-source news analysis and mining.